Journal
JOURNAL OF PERSONALIZED MEDICINE
Volume 11, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/jpm11080785
Keywords
network inference; integrative biology; rheumatoid arthritis; signaling cascades; gene regulation; transcription factors; Boolean simulations; systems biology
Funding
- University of Paris Saclay, France
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Rheumatoid arthritis is a complex autoimmune disease with various genetic and environmental factors. By using systems biology approaches and machine learning, researchers aim to uncover mechanisms regulating key transcription factors and derive patient-specific models. Integrating multiple data types can help analyze disease heterogeneity and treatment response in a more comprehensive manner.
Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients' data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.
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